In many areas of industry and society, including energy, healthcare, and logistics, agents collect vast amounts of data that are deemed proprietary. These data owners extract predictive information of varying quality and relevance from data depending on quantity, inherent information content, and their own technical expertise. Aggregating these data and heterogeneous predictive skills, which are distributed in terms of ownership, can result in a higher collective value for a prediction task. In this paper, a platform for improving predictions via the implicit pooling of private information in return for possible remuneration is envisioned. Specifically, a wagering-based forecast elicitation market platform has been designed, in which a buyer intending to improve their forecasts posts a prediction task, and sellers respond to it with their forecast reports and wagers. This market delivers an aggregated forecast to the buyer (pre-event) and allocates a payoff to the sellers (post-event) for their contribution. A payoff mechanism is proposed and it is proven that it satisfies several desirable economic properties, including those specific to electronic platforms. Furthermore, the properties of the forecast aggregation operator and scoring rules are discussed in order to emphasize their effect on the sellers’ payoff. Finally, numerical examples are provided in order to illustrate the structure and properties of the proposed market platform.

The main themes of this thesis are the design and analysis of payoff distribution methods for situations where agents collaborate to generate a utility. For modeling such scenarios, we majorly focus on the coalitional game theoretic framework that provides mathematical formalism to study the behavior of rational agents when they cooperate for selfish interests [69]. We utilize the tools from coalitional game theory to develop mechanisms for demand-side energy management, namely, energy coalitions, peer-to-peer energy trading (P2P), and real-time local electricity markets, that can help accelerate the energy transition [106]. For the solution of resulting games, we design distributed algorithms that converge to a payoff distribution characterized by stability and fairness. The primary approach to convergence analysis of proposed algorithms relies on the operator theory and fixed-point iterations. Finally, we also propose payoff distribution criteria for a wagering-based forecasting market that can help energy generation sources to improve their forecast....

In this article, we propose a bilateral peer-to-peer (P2P) energy trading scheme under single-contract and multi-contract market setups, both as an assignment game, a special class of coalitional games. The proposed market formulation allows for efficient computation of a market equilibrium while keeping the desired economic properties offered by the coalitional games. Furthermore, our market model allows buyers to have heterogeneous preferences (product differentiation) over the energy sellers, which can be economic, social, or environmental. To address the problem of scalability in coalitional games, we design a novel distributed negotiation mechanism that utilizes the geometric structure of the equilibrium solution to improve the convergence speed. Our algorithm enables market participants (prosumers) to reach a consensus on a set of 'stable' and 'fair' bilateral contracts which encourages prosumer participation. The negotiation process is executed with virtually minimal information requirements on a time-varying communication network that in turn preserves privacy. We use operator-theoretic tools to rigorously prove its convergence. Numerical simulations illustrate the benefits of our negotiation protocol and show that the average execution time of a negotiation step is much faster than the benchmark.

In this article, we consider a sequence of transferable utility coalitional games, where the actual coalitional values are unknown but vary within known bounds. As a solution to the resulting family of games, we formalize the notion of 'robust core.' Our main contribution is to design two distributed algorithms, namely 1) distributed payoff allocation and 2) distributed bargaining, which converge to a consensual payoff distribution in the robust core. We adopt an operator-theoretic perspective to show convergence of both algorithms executed on time-varying communication networks. An energy storage optimization application motivates our framework for 'robust coalitional games.'

In the context of coalitional games, we present a partial operator-theoretic characterization of the approachability principle and, based on this characterization, we interpret a particular distributed payoff allocation algorithm to be a sequence of time-varying paracontractions. Then, we also propose a distributed payoff allocation algorithm on time-varying communication networks. The state in the proposed algorithm converges to a consensus in the”CORE” set as desired. For the convergence analysis, we rely on an operator-theoretic property of paracontraction.

In this paper, we propose a bilateral peer-to-peer (P2P) energy trading scheme for residential prosumers with a simplified entry to the market. We formulate the market as an assignment game, a special class of coalitional games. For solving the resulting decision problem, we design a bilateral negotiation mechanism that enables matched buyer-seller pairs to reach a consensus on a set of 'stable' and 'fair' trading contracts. The proposed negotiation process can be executed on possibly time-varying communication networks with virtually minimal information requirements that in turn preserves privacy among prosumers. Numerical simulations illustrate the beneficial features of our P2P market model and negotiation protocol.